Computing and Informatics (E-Journal - Institute of Informatics, SAS, Bratislava)
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A Framework for Self-Inspection Buildings Based on Augmented Reality Agents
Get PDFEmergent technologies are being adopted at all the stages of smart building lifecycles. More specifically, mobile, pervasive, and communication technologies are being deployed to achieve a wide range of functions that improve the building performance (including ventilation, air conditioning, heating, lighting, and security) and reduce their related costs. Augmented Reality (AR) has arisen as a promising tool to achieve these goals. However, in spite of the multiple solutions that have integrated AR within smart buildings, several shortcomings are yet to be solved. In addition to the limited user experience and the lack of AR content, current solutions do not provide effective collaborations between construction stakeholders as well as do not include intelligent mechanisms for the management of inspection activities. In order to address some of the smart building challenges, we are proposing in this paper a new framework for intelligent collaborative self-inspection buildings based on the concept of awareness wheel as well as the multi-agent system paradigm
SingTRACeX: Navigation System to Address Wandering Behavior for Elders and Their Caregivers
Get PDFThe issue of an ever-increasing ageing population has been the increasing burden on caregivers to care for the elderly population. Caring for elders, especially those diagnosed with dementia, can be challenging. People living with dementia (PWD) require extra care and attention from the caregivers due to the associated behaviours that come with dementia. Wandering is a frequent behaviour exhibited by PWD, which can bring about negative outcomes on the PWD as well as increasing the stress of the caregivers. Though many technological solutions exist, they are not widely deployed. This paper introduces a technological framework, bridging the localisation technologies to the needs of elders and caregivers. The aim is to minimise or eliminate the negative outcomes of dementia wandering and to reduce the burden and stress on the caregivers, thus improving overall well-being. In this paper, we study the application, SingTRACeX, features by considering user needs from the field study with 2 focus group discussions (FGD), comprising of 14 professional caregivers and coordinators. The proposed system features Real-time Location Tracking and Indoor Localisation. The location is determined by GPS location from the Sensor module when outdoors, and estimation using data from the WiFi module, and Bluetooth module when indoors. The indoor navigation provided by the Indoor Localisation module uses an A-star search algorithm. This paper could serve as a foundation that can be built upon over time as the needs of elders and caregivers may change over time, as well as the evolution of technology that may bring about new methods to address needs
Multifactorial Evolutionary Algorithm for Simultaneous Solution of TSP and TRP
Get PDFWe study two problems called the Traveling Repairman Problem (TRP) and Traveling Salesman Problem (TSP). The TRP wants to minimize the total time for all customers that have to wait before being served, while the TSP aims to minimize the total time to visit all customers. In this sense, the TRP takes a customer-oriented view, whereas the TSP is server-oriented. In the literature, there exist numerous algorithms that are developed for two problems. However, these algorithms are designed to solve each problem independently. Recently, Multifactorial Evolutionary Algorithm (MFEA) has been a variant of Evolutionary Algorithm (EA) aiming to solve multiple optimization tasks simultaneously. The MFEA framework has yet to be fully exploited, but the realm has recently attracted much interest from the research community. This paper proposed a new approach using the MFEA framework to solve these two problems simultaneously. The MFEA has two tasks simultaneously: the first is solving the TRP problem, and the second is solving the TSP. Experiment results show the efficiency of the proposed MFEA: 1. for small instances, the algorithm reaches the optimal solutions of both problems; 2. for large instances, our solutions are better than those of the previous MFEA algorithms
Accurate Analysis of the Spatial Pattern of Reflected Light and Surface Orientations Based on Color Illumination
Get PDF3D Recovery approaches require a variety of clues to obtain shape information. The shape from shading (SFS) method uses shading information in images to estimate depth maps. Although shading contains detailed information, it causes some well-known ambiguities such as convex-concave ambiguity. In this study, a system installation, using red, green, and blue illumination, and an algorithm, processing reflections on the surface, were proposed for the accurate analysis of surface orientations, and ambiguity problems. Surface orientations, erroneously predicted by six different methods, were improved by implementing the proposed system. Consequently, the correct orientation of the surface points was determined by removing the ambiguities in images taken without considering the location of illumination, and all the tested methods provided successful results using the proposed system
Fault Diagnosis of Discrete-Event Systems from Abstract Observations
Get PDFActive systems (ASs) are a special class of (asynchronous) discrete-event systems (DESs). An AS is represented by a network of components, where each component is modeled as a communicating automaton. Diagnosing a DES amounts to finding out possible faults based on the DES model and a sequence of observations gathered while the DES is being operated. This is why the diagnosis engine needs to know what is observable in the behavior of the DES and what is not. The notion of observability serves this purpose. In the literature, defining the observability of a DES boils down to qualifying the state transitions of components either as observable or unobservable, where each observable transition manifests itself as an observation. Still, looking at the way humans observe reality, typically by associating a collection of events with a single, abstract perception, the state-of-the-art notion of DES observability appears somewhat narrow. This paper presents, a generalized notion of observability, where an observation is abstract rather than concrete, since it is associated with a DES behavioral scenario rather than a single component transition. To support the online diagnosis engine, knowledge compilation is performed offline. The outcome is a set of data structures, called watchers, which allow for the tracking of abstract observations
Using Machine Learning Techniques to Support the Emergency Department
Get PDFThis research lays down foundations for a stronger presence of machine learning in the emergency department. Using machine learning to make predictions on a patient's situation can increase patient's health and decrease the waiting time. This paper explores to what extent it is possible to accurately predict ER outcome. These predictions will be based on routinely available ER data from a Dutch hospital. The data set used is representative for any Dutch Hospital. Prediction performance is compared between ML predictors. Using random forest and stacked ensemble gathered the best results. This research found that for more than half of the adult patients, the algorithm can very accurately predict hospitalization, with similar results for children and during the COVID-19. Moreover, it is investigated which characteristics and events contribute to the direction of the patient. Finally, several plans are introduced to substantially improve the ER process, for example by quickly reviewing patients selected by the algorithms. These might lead to an ER process that is significantly quicker, with more accurate diagnosis
VR Scenarios to Treat Mental Health
Get PDFSchizophrenia is characterized by delusions, hallucinations, anhedonia and apathy, and is a chronic mental illness, which still has no cure. It is diagnosed in three main groups, namely positive symptoms, negative symptoms, and cognitive symptoms, and affects the patient in major areas of life, such as work, interpersonal relationships, or self-care. The usual treatment is carried out with the help of antipsychotic medications, which mainly target the positive symptoms of the illness, but have little effect on the negative symptoms of schizophrenia. It is a disease that affects 1 % of the population, and while it is not the most common of other mental disorders, it can be the most disabling. Virtual reality (VR) is increasingly used as a powerful auxiliary tool in rehabilitation. It allows an immersive audio-visual environment with high clinical relevance and robust validity, modernizing rehabilitation interventions, leading to improved motor function and biomechanical or diagnostic ability better than traditional methods used to treat patients under cognitive health. In this context, this paper addresses the design of virtual environments and computer simulations, providing the patient with an experience close to the real world, and allowing intensive repetition of essential tasks during the mental health rehabilitation process, with real-time feedback in a controlled and safe environment. Virtual reality (VR) is increasingly used as a powerful auxiliary tool in rehabilitation
A Novel Data Analytic Model for Mining User Insurance Demands from Microblogs
Get PDFThis paper proposes a method based on LDA model and Word2Vec for analyzing Microblog users' insurance demands. First of all, we use LDA model to analyze the text data of Microblog user to get their candidate topic. Secondly, we use CBOW model to implement topic word vectorization and use word similarity calculation to expand it. Then we use K-means model to cluster the expanded words and redefine the topic category. Then we use the LDA model to extract the keywords of various insurance information on the “Pingan Insurance” website and analyze the possibility of users with different demands to purchase various types of insurance with the help of word vector similarity. Finally, the validity of the method in this paper is verified against Microblog user information. The experimental results show that the accuracy, recall rate and F1 value of the LDA-CBOW extending method have been proposed compared with that of the traditional LDA model, respectively, which proves the feasibility of this method. The results of this paper will help insurance companies to accurately grasp the preferences of Microblog users, understand the potential insurance needs of users timely, and lay a foundation for personalized recommendation of insurance products
General Deep Multinomial Logit Model
Get PDFMultinomial logit model (MNL) is by far the most widely used discrete choice model that is widely used to explain or predict a choice from a set of two or more discrete alternatives. MNL operates within a framework of the random utility model (RUM) in which the utility of an alternative perceived by an individual consists of two components: systematic component and random component. The systematic component is usually defined as a linear function. However, practical decision processes involve complex considerations regarding various aspects of the alternatives and individual which cannot be adequately represented by simple linear models. To overcome the weakness of linear utility model and improve the performance of MNL, in this paper, we propose a general deep multinomial logit model (GDMNL) that takes advantage of both traditional MNL and deep learning. In this model, deep neural networks are applied to extend MNL by learning different nonlinear utility functions of various alternatives. The empirical study in the domain of transit route choice analysis demonstrates the validity and superiority of the proposed model